Senior Site Reliability Engineer

Role details

Meet the Team The Network Assurance Data Platform team within Cisco ThousandEyes is responsible for building, operating, and scaling the core data infrastructure that powers large-scale network assurance, analytics, and intelligence capabilities. The team manages critical cloud infrastructure, big data workflows, ML platform components, and reliability engineering practices across AWS environments. We focus on improving platform reliability, scalability, performance, automation, and cost efficiency while enabling engineering and data teams to deliver business-critical capabilities at scale. As a Senior Site Reliability Engineer in the NADP team, you will work on highly scalable infrastructure supporting data pipelines, ML workloads, cloud-native services, and cost-optimized AWS operations. Your Impact Own and operate scalable, reliable, and cost-efficient infrastructure for the ThousandEyes Network Assurance Data Platform. Manage, optimize, and improve large-scale data workflows using Apache Airflow, AWS EMR, Spark, and Hadoop-based processing platforms. Operate and improve Amazon EKS environments supporting containerized services, ML workloads, and production-grade platform components. Build and maintain infrastructure automation using Terraform and other infrastructure-as-code practices. Develop Python-based automation, integrations, operational tooling, reporting, and reliability improvements. Drive FinOps practices across NADP infrastructure, including cost visibility, cost allocation, forecasting, anomaly detection, optimization, and governance. Partner with data engineering, ML, platform, finance, and product teams to improve reliability, performance, scalability, and cost efficiency. Identify infrastructure bottlenecks, performance issues, inefficient workloads, and cost optimization opportunities. Improve observability, alerting, incident response, and operational readiness across data and ML platforms. Support capacity planning, right-sizing, autoscaling, storage optimization, and workload efficiency across AWS services. Lead technical discussions, influence design decisions, and guide teams toward reliable and cost-conscious architecture. Provide senior-level technical leadership, mentorship, and operational guidance to engineers across the team. Drive continuous improvement in platform reliability, automation, deployment practices, and operational excellence. Minimum Qualifications Bachelor’s degree or higher in Engineering, Computer Science, or equivalent practical experience. 8–10 years of relevant experience in Site Reliability Engineering, DevOps, Cloud Infrastructure, Platform Engineering, Data Infrastructure, or Production Engineering. Strong experience operating production infrastructure on AWS. Strong experience with Apache Airflow for workflow orchestration, pipeline operations, scheduling, monitoring, and troubleshooting. Experience with AWS EMR, Spark, Hadoop, or similar large-scale data processing platforms. Strong experience operating Amazon EKS or Kubernetes-based environments in production. Experience supporting containerized workloads, preferably including ML workloads or data platform services. Strong experience with Terraform and infrastructure-as-code practices. Strong Python programming or scripting experience for automation, integrations, operational tooling, and infrastructure workflows. Practical experience with cloud cost optimization, FinOps, AWS cost analysis, tagging, budgeting, forecasting, and cost governance. Strong understanding of Linux systems, networking, distributed systems, and production troubleshooting. Experience with observability tools such as CloudWatch, Prometheus, Grafana, Splunk, OpenSearch, Datadog, or similar platforms. Experience with incident management, production support, root cause analysis, reliability improvements, and operational excellence. Ability to analyze infrastructure, performance, and cost data and convert findings into clear technical recommendations. Strong communication skills with the ability to collaborate across data, ML, platform, finance, and engineering teams. Ability to operate independently, drive initiatives end to end, and provide technical leadership in a fast-paced environment. Preferred Qualifications Experience working with large-scale SaaS platforms or high-volume data infrastructure. Experience with ML infrastructure, model execution platforms, batch processing, or data pipeline reliability. Experience optimizing EMR, Spark, Airflow, EKS, storage, and compute workloads for performance and cost. Experience with AWS services such as EC2, S3, RDS, IAM, VPC, CloudWatch, OpenSearch, Lambda, ElastiCache, and related cloud-native services. Experience with AWS Savings Plans, Reserved Instances, Spot adoption, Graviton migration, storage lifecycle management, and workload right-sizing. Experience with cloud cost management tools such as AWS Cost Explorer, AWS CUR, Cloudability, CloudHealth, Kubecost, or similar platforms. Experience driving FinOps programs, cost reviews, stakeholder reporting, OKR tracking, and executive-level updates. Experience with CI/CD systems, GitHub workflows, Atlantis, or similar deployment automation platforms. Experience with Puppet, Ansible, Helm, Argo CD, or other configuration and deployment management tools. FinOps certification or equivalent cloud financial management experience is a plus. Ability to influence engineering teams toward cost-aware, scalable, and reliable design patterns. What Success Looks Like NADP data and ML infrastructure is reliable, scalable, performant, and cost-efficient. Airflow, EMR, Spark, and EKS workloads are operated with strong observability, automation, and production maturity. AWS cost visibility, forecasting, and governance are improved across NADP-owned infrastructure. Cloud wastage is reduced through right-sizing, automation, storage optimization, and workload efficiency improvements. Infrastructure is managed through production-grade Terr